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Survey Reveals Perceived Challenges and Benefits of “As-a-Service” Models for Analytics Editor’s Note: This report contains proprietary statistical, anecdotal, and observational researc

Migrating Big Data Analytics into the Cloud

Mike Barlow

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Mike Barlow

Migrating Big Data Analytics into the Cloud

Migrating Big Data Analytics into the Cloud

by Mike Barlow

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Table of Contents

Survey Reveals Perceived Challenges and Benefits of “As-a-Service”

Models for Analytics 1

Into the Cloud 1

Current and Planned Use of Analytics 2

Cloud Applications 4

As-a-Service Models 5

Areas Where Additional Help Might be Necessary 5

Reasons for Reluctance 5

Unexpected Costs 7

iii

Survey Reveals Perceived Challenges and Benefits of

“As-a-Service” Models for Analytics

Editor’s Note: This report contains proprietary statistical, anecdotal, and observational research on the current state of big data analytics in the cloud We are sharing the information for the benefit of users, decision-makers, and suppliers operating within the big data analytics community For the purposes of this paper, we are including all frame‐ works for managing big data (e.g., relational, non-relational, NoSQL), regardless of the underlying architecture.

Into the Cloud

Despite the steady migration of numerous IT capabilities into the cloud, many organizations have been reluctant to embrace the idea of

“big data-as-a-service.” On one hand, cloud-based big data analytics squarely address ongoing issues of scale, speed, and cost On the other hand, they also create new issues around privacy, latency, and veracity Oftentimes, the best way to gain insight into a complex technology problem is by digging beneath the surface and surveying the percep‐ tions of the user community John King, a data analyst at O’Reilly Me‐ dia, designed and conducted a survey of the O’Reilly community, which typically includes software developers, systems architects, en‐ gineers, data scientists, and data analysts The survey was conducted from July 9 through August 3, 2014 There were 312 respondents from various industries including technology, healthcare, finance, and tel‐ ecom

1

One of the main takeaways from the survey is that after an organization has gained some experience using big data in the cloud, it is more likely

to expand its use of similar big data services In other words, once they’ve tested the waters, they’re more likely to jump into the pool That insight is neither surprising nor illogical Humans are hard-wired

to be suspicious of novelty, and many executives still regard the cloud

as something new and largely unexplored For suppliers of big data in the cloud solutions, the primary challenge is helping customers take the first steps

According to our survey, the market seems ready for that kind of ap‐ proach The survey shows that roughly 40% of respondents who iden‐ tify themselves as big data practitioners currently use cloud services for analytics, slightly more than 30% are not, and slightly less than 30% are planning to in the future

Moreover, the survey shows that roughly 55% of respondents who plan

to become big data practitioners also plan to use cloud services for analytics, compared to roughly 45% who said they would not

Current and Planned Use of Analytics

The survey also showed that more than 70% of respondents currently use or plan to use predictive analytics and business intelligence/ reporting capabilities Roughly 60% currently use or plan to use big data analytics for text mining or machine learning, and slightly less than 50% currently use or plan to use big data analytics for hypothesis testing The poll results correspond with anecdotal research suggest‐ ing that big data is perceived mainly as a platform for advanced ana‐ lytics and enhanced BI

Interestingly, only about 40% of respondents indicated they currently use or plan to use big data for social network analysis, which seems counterintuitive based on the sheer volume of media coverage around social media topics

2 | Survey Reveals Perceived Challenges and Benefits of “As-a-Service” Models for Analytics

The survey also revealed that more than 80% of respondents who cur‐ rently use or intend to deploy cloud-based analytics solutions cite

“flexibility to scale up or down” as a reason for choosing a service model over an on-premises delivery model The next most popular reason cited was faster deployments, followed by reduced capital ex‐ penditures, access to advanced technologies, and lack of skills required for managing on-premises analytics

Those findings correspond with the expressed needs of IT executives and other corporate-level decision-makers, who are often quoted as saying the cloud offers the freedom to test new technologies quickly and inexpensively It also plays to the seasonality of industries such as retail and energy, which often handle vastly different amounts of data

at different times of the year

About 60% of respondents said that within the next 18 months, they planned to deploy a discovery environment for data mining or other

Survey Reveals Perceived Challenges and Benefits of “As-a-Service” Models for Analytics | 3

forms of statistical analysis Slightly more than 50% said they would deploy an open source Hadoop framework or NoSQL database, while slightly less than 50% indicated they were planning to deploy a rela‐ tional database Clearly, users and decision-makers are still hedging their bets

Cloud Applications

Drilling down into the big data stack, slightly more than 65% of re‐ spondents indicated they were currently using or planned to use cloud-based data storage and management applications Slightly more than 60% indicated they are using or plan to use analytic sandboxes, while slightly more than 50% said they would use cloud-based services for testing and development

About 40% of respondents said they use or plan to use the cloud for production data warehousing, while slightly more than 30% said they would use the cloud for data marts Only about 12% said they would use the cloud for disaster recovery, which is somewhat surprising and signals a potential opportunity for cloud vendors

4 | Survey Reveals Perceived Challenges and Benefits of “As-a-Service” Models for Analytics

As-a-Service Models

In a ranking of cloud-based “as-a service” models, respondents cited platform-as-a-service, followed by infrastructure-as-a-service, software-as-a-service, and database-as-a-service Again, this seems to follow larger IT trends, in which organizations follow paths of least resistance to achieve the highest perceived value at any given time

Areas Where Additional Help Might be

Necessary

Respondents who indicated they were planning to use cloud-based data analytics in the future were also asked to rank areas in which they would require help moving analytics into the cloud The survey results showed concern around daily data management activities (e.g., secu‐ rity administration, performance and workload management, and backup); ongoing application development; daily data integration is‐ sues; and daily management of business intelligence environments

Reasons for Reluctance

Respondents who indicated they do not use cloud-based analytics were asked to choose the main reasons for their reluctance Most chose

Survey Reveals Perceived Challenges and Benefits of “As-a-Service” Models for Analytics | 5

data and privacy requirements as the primary reason, followed by ex‐ isting on-premises capabilities, connectivity and bandwidth issues, and performance concerns

Those findings correspond with anecdotal and observational research

It seems clear that balancing the benefits and risks of a big data cloud strategy can be a daunting task Based on our interviews with users and subject matter experts, advantages include scalability, elasticity, agility, lower cost, and rapid innovation The areas of most concern are performance, security, bandwidth, and data accuracy

Table 1 Benefits and challenges of moving big data into the cloud.

Benefits/Advantages Obstacles/Concerns

Scalability/elasticity Performance

Agility Security

Cost Bandwidth

Time to market Data accuracy/veracity

“With the cloud, you’re always going to be cutting edge with the push

of a button or the swipe of a credit card,” said Marc Clark, director of cloud strategy and deployment at Teradata “Cloud vendors will con‐ tinue adding new features to keep ahead of their competitors, which means that even smaller companies can use the latest technology You just cannot do that with on-premises solutions I know of many com‐ panies with on-premises technology that is two or three generations behind what’s currently available through the cloud.”

Flexibility is a main driver of cloud adoption, according to Clark “The cloud lets you weigh the advantages and disadvantages of a system without committing the resources that would be required if you were

6 | Survey Reveals Perceived Challenges and Benefits of “As-a-Service” Models for Analytics

going to buy it or enter into a multi-year licensing deal with a vendor,”

he said “That’s the beauty of the cloud You can test something for two

or three weeks and see if it’s right for you, without having to give up your right arm.”

The ability to test systems in the cloud, in much the same way that a consumer test drives a new car before buying it, helps companies overcome their reluctance to adopt cloud-based data warehouse serv‐ ices “Some companies worry that performance levels will be com‐ promised in the cloud The best way to find out is by trying out a cloud-based service for a couple of weeks Then you get to test your assump‐ tions and discover for yourself if the cloud makes sense for your or‐ ganization,” said Clark

Some companies might discover they have bandwidth issues that would prevent them from taking advantage of a cloud-based service Some might decide to upgrade their network connectivity, while oth‐ ers might decide to stick with their on-premises solution “What’s im‐ portant is that you find out in minutes or hours, instead of finding out

in weeks or months,” said Clark “You will know very quickly whether the cloud is meeting your performance needs.”

For some IT executives, moving workloads into the cloud represents

a potential loss of control “If you feel the need to tuck your servers in

at night and tell them a bedtime story, then there might be a problem,” said Clark “Some people see a disadvantage in not owning the hard‐ ware and the software They feel as though they are losing control Or they hear about someone who moved workloads into the cloud, and then moved them back on premises The cloud is not the right solution for everybody.”

Unexpected Costs

Stories about organizations encountering unexpected costs when moving into the cloud are common Unrealistic expectations gener‐ ated by endless media hype about the unparalleled virtues of the cloud can set the stage for disappointment “If your primary motivation is cost reduction, don’t move into the cloud,” said Clark “Lots of people think cost is the number-one reason to choose the cloud, but that’s the wrong way to look at it, especially if you’re planning on moving your big data analytics into the cloud.”

Survey Reveals Perceived Challenges and Benefits of “As-a-Service” Models for Analytics | 7

When you’re building a business case for the cloud, your primary considerations should be speed, scalability, and flexibility “The cloud enables business agility The cloud is elastic, so if your business sud‐ denly grows you can respond very quickly if you’re in the cloud The same holds true if your market shrinks You can move in either direc‐ tion much more rapidly and with much more freedom,” said Clark Paul Barsch, a services marketing director at Teradata, recommends taking the time to perform due diligence reviews with potential cloud vendors before moving forward with a services plan “Make sure the supplier provides basic cloud infrastructure functions and walk the supplier through an itemized checklist of your requirements,” he said

A typical checklist would include:

1 Hardware/software monitoring and maintenance

2 Security administration

3 Resource provisioning

4 Networking

5 On-boarding

6 Data center management

7 Backup and recovery

8 System availability

9 DBA support

10 Daily operational management

Ideally, cloud-service suppliers should also provide high-level logical and physical data models, industry report templates, consulting (when necessary), data integration management, data migration, and other capabilities required for launching successful cloud implementations

In a 2013 article coauthored with Ed White, general manager for Ter‐ adata Cloud Solutions, Barsch advised against focusing solely on “low‐ est cost per terabyte” solutions and noted that “it’s important to rec‐ ognize that not all cloud infrastructures are created equal.” Some kinds

of cloud infrastructures are better at handling data analytics than oth‐ ers, so make sure the supplier has the appropriate infrastructure for supporting your analytic workloads

Since analytics tend to be CPU- and I/O-intensive, “it does not make sense to run analytic workloads on cloud infrastructures that are pro‐

8 | Survey Reveals Perceived Challenges and Benefits of “As-a-Service” Models for Analytics